# BAGNet: Bidirectional Aware Guidance Network for Malignant Breast lesions Segmentation

arXiv: https://arxiv.org/abs/2204.13342 · HTML: https://mlsanity.com/paper/2204.13342/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 33 references that are in the map.
- First posted on arXiv 28 Apr 2022.

## Builds on (most cited first, 12 of 12)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation](https://mlsanity.com/paper/1511.00561/) (2015)
- [Attention U-Net: Learning Where to Look for the Pancreas](https://mlsanity.com/paper/1804.03999/) (2018)
- [UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation](https://mlsanity.com/paper/1912.05074/) (2019)
- [UNet 3+: A Full-Scale Connected UNet for Medical Image Segmentation](https://mlsanity.com/paper/2004.08790/) (2020)
- [Global Guidance Network for Breast Lesion Segmentation in Ultrasound Images](https://mlsanity.com/paper/2104.01896/) (2021)
- [X-Net: Brain Stroke Lesion Segmentation Based on Depthwise Separable Convolution and Long-range Dependencies](https://mlsanity.com/paper/1907.07000/) (2019)
- [Automatic Breast Ultrasound Image Segmentation: A Survey](https://mlsanity.com/paper/1704.01472/) (2017)
- [Stan: Small tumor-aware network for breast ultrasound image segmentation](https://mlsanity.com/paper/2002.01034/) (2020)
- [Automatic kidney segmentation in ultrasound images using subsequent boundary distance regression and pixelwise classification networks](https://mlsanity.com/paper/1811.04815/) (2018)
- [Fuzzy Semantic Segmentation of Breast Ultrasound Image with Breast Anatomy Constraints](https://mlsanity.com/paper/1909.06645/) (2019)

## Built on by (most cited first, 4 of 4)

- [Rethinking the Unpretentious U-net for Medical Ultrasound Image Segmentation](https://mlsanity.com/paper/2209.07193/) (2022)
- [ESKNet-An enhanced adaptive selection kernel convolution for breast tumors segmentation](https://mlsanity.com/paper/2211.02915/) (2022)
- [LightBTSeg: A lightweight breast tumor segmentation model using ultrasound images via dual-path joint knowledge distillation](https://mlsanity.com/paper/2311.11086/) (2023)
- [DSU-Net: An Attention-Enhanced Dense Skip U-Net for Breast Lesion Segmentation in Mammographic Images](https://mlsanity.com/paper/2606.06537/) (2026)

## Abstract (arXiv)

Breast lesions segmentation is an important step of computer-aided diagnosis system, and it has attracted much attention. However, accurate segmentation of malignant breast lesions is a challenging task due to the effects of heterogeneous structure and similar intensity distributions. In this paper, a novel bidirectional aware guidance network (BAGNet) is proposed to segment the malignant lesion from breast ultrasound images. Specifically, the bidirectional aware guidance network is used to capture the context between global (low-level) and local (high-level) features from the input coarse saliency map. The introduction of the global feature map can reduce the interference of surrounding tissue (background) on the lesion regions. To evaluate the segmentation performance of the network, we compared with several state-of-the-art medical image segmentation methods on the public breast ultrasound dataset using six commonly used evaluation metrics. Extensive experimental results indicate that our method achieves the most competitive segmentation results on malignant breast ultrasound images.